Location- and Density-Based Hierarchical Clustering Using Similarity Analysis

Authors:
Peter Bajcsy;Narendra Ahuja
Affiliations:
Cognex Corp, Portland, OR;Univ. of Illinois, Urbana
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1998

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Abstract

This paper presents a new approach to hierarchical clustering of point patterns. Two algorithms for hierarchical location- and density-based clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations or point separations. Performance of the clustering methods is compared with four other methods. The approach is applied to a two-step texture analysis, where points represent centroid and average color of the regions in image segmentation.